Using R in hydrology (EGU2017 short course)

These slides and all other course material can be found at

github.com/brry/rhydro

This is an R Markdown Notebook.

For discussions, please visit the Hydrology in R Facebook group

Contents of this workshop

• Good coding practice, report generation (Rstudio, rmarkdown, R notebook)
• Using R as GIS (reading a rainfall shapefile + Kriging, sf + leaflet + mapview + OSMscale)
• River discharge time-series visualisation and extreme value statistics (animation + extremeStat)
• Hydrological modelling with airGR
• Exploratory Data Analysis including flow duration curve and trend analysis on time-series

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report

Good coding practice, report generation (Rstudio, rmarkdown, R notebook)
Daniel Kotz

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gis

Using R as GIS (reading a rainfall shapefile + Kriging, sf + leaflet + mapview + OSMscale)
Berry Boessenkool

shapefiles

Reading shapefiles with maptools::readShapeSpatial and rgdal::readOGR is obsolete.
Instead, use sf::st_read. sf is on CRAN since oct 2016.

rain <- sf::st_read("data/PrecBrandenburg/niederschlag.shp")

Reading layer `niederschlag' from data source `S:\Dropbox\R\rhydro\presentations\data\PrecBrandenburg\niederschlag.shp' using driver `ESRI Shapefile'
converted into: POLYGON
Simple feature collection with 277 features and 1 field
geometry type:  POLYGON
dimension:      XY
bbox:           xmin: 3250175 ymin: 5690642 xmax: 3483631 ymax: 5932731
epsg (SRID):    NA
proj4string:    +proj=tmerc +lat_0=0 +lon_0=15 +k=0.9996 +x_0=3500000 +y_0=0 +ellps=GRS80 +units=m +no_defs

centroids <- sf::st_centroid(rain)
centroids <- sf::st_coordinates(centroids)

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Plotting, maps

Static plot:

plot(rain[,1])

Static map:

prj <- sf::st_crs(rain)$proj4string
centroids <- as.data.frame(centroids)
cent_ll <- OSMscale::projectPoints(Y,X, data=centroids, to=OSMscale::pll(), from=prj)
map_static <- OSMscale::pointsMap(y,x, cent_ll, fx=0.08, type="maptoolkit-topo", zoom=6)

Downloading map with extend 11.029332, 14.981464, 51.10418, 53.765423 ...
Done. Now plotting...

Interactive map:

library(leaflet)
cent_ll$info <- paste0(sample(letters,nrow(cent_ll),TRUE), ", ", round(cent_ll$x,2), 
                       ", ", round(cent_ll$y,2))
leaflet(cent_ll) %>% addTiles() %>% addCircleMarkers(lng=~x, lat=~y, popup=~info)

Interactive map of shapefile:

# devtools::install_github("environmentalinformatics-marburg/mapview", ref = "develop")
library(berryFunctions) # classify, seqPal
col <- seqPal(n=100, colors=c("red","yellow","blue"))[classify(rain$P1)$index]
mapview::mapview(rain, col.regions=col)

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Kriging

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discharge

River discharge time-series visualisation and extreme value statistics (animation + extremeStat)
Berry Boessenkool

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hydmod

Hydrological modelling with airGR
Katie Smith

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eda

Exploratory Data Analysis including flow duration curve and trend analysis on time-series
Shaun Harrigan

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